Arguments

(Optional) A tbl_spark. If provided, eagerly fit the (estimator)
feature "transformer" against dataset. See details.

uid

A character string used to uniquely identify the feature transformer.

...

Optional arguments; currently unused.

features

The columns to use in the principal components
analysis. Defaults to all columns in x.

pc_prefix

Length-one character vector used to prepend names of components.

Details

When dataset is provided for an estimator transformer, the function
internally calls ml_fit() against dataset. Hence, the methods for
spark_connection and ml_pipeline will then return a ml_transformer
and a ml_pipeline with a ml_transformer appended, respectively. When
x is a tbl_spark, the estimator will be fit against dataset before
transforming x.

When dataset is not specified, the constructor returns a ml_estimator, and,
in the case where x is a tbl_spark, the estimator fits against x then
to obtain a transformer, which is then immediately used to transform x.

ml_pca() is a wrapper around ft_pca() that returns a
ml_model.

Value

The object returned depends on the class of x.

spark_connection: When x is a spark_connection, the function returns a ml_transformer,
a ml_estimator, or one of their subclasses. The object contains a pointer to
a Spark Transformer or Estimator object and can be used to compose
Pipeline objects.

ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with
the transformer or estimator appended to the pipeline.

tbl_spark: When x is a tbl_spark, a transformer is constructed then
immediately applied to the input tbl_spark, returning a tbl_spark